Statistics 2 – Inference and Association

Statistics 2 – Inference and Association

This course will teach you the use of inference and association through a series of practical applications, based on the resampling/simulation approach, and how to test hypotheses, compute confidence intervals regarding proportions or means, computer correlations, and use of simple linear regressions.

This course, the second of a three-course sequence, will teach you the use of inference and association through a series of practical applications, based on the resampling/simulation approach, and how to test hypotheses, compute confidence intervals regarding proportions or means, computer correlations, and use of simple linear regressions.

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Overview

This course is the second in a three-course sequence that provides an easy introduction to inference and association through a series of practical applications based on the resampling/simulation approach. Once you have completed this course you will be able to test hypotheses and compute confidence intervals regarding proportions or means, compute correlations and fit simple linear regressions.
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Learning Outcomes

After completing this course, you will be able to test hypotheses and compute confidence intervals regarding proportions or means, compute correlations and fit simple linear regressions.

  • Calculate a confidence interval for a proportion
  • Conduct an A-B test
  • Calculate the correlation coefficient and test its statistical significance
  • Fit a simple regression line via least squares
  • Use the regression equation for predicting
  • Fit a multiple regression model
  • Distinguish between explanation and prediction in regression
  • Assess regression model fit (R-squared, goodness-of-fit, RMSE)
  • Interpret regression coefficients
  • Explain the use of k-nearest neighbors for prediction
  • Use a hold-out sample to assess performance of models

Who Should Take This Course

Anyone with no prior statistical background, who encounter statistics in their work, or who needs introductory statistics for further study.

Instructors

Mr. Peter Bruce

Mr. Peter Bruce

Mr. Peter Bruce is Founder and President of The Institute for Statistics Education at Statistics.com. He is the developer of Resampling Stats software (originated by Julian Simon in the 1970's), and has also taught resampling statistics at the University of Maryland and in a variety of short courses. He is the author of Data Mining for Business Analytics, with Galit Shmueli, Peter Gedeck, Inbal Yahav and Nitin R. Patel (Wiley, 3r...

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Anuja Kulkarni

Ms. Anuja Kulkarni

Ms. Anuja Kulkarni has managed and taught over 125 online course sessions and more than 1000 students as an Assistant Teacher at The Institute for Statistics Education. She holds a Masters’ degree in Statistics from Kolhapur University, India, where she also taught undergraduate statistics. Ms. Kulkarni teaches Statistics, Optimization Methods and Predictive Analytics and assists in several other course topics here for over six years. In all, her passion is leading new students into the fascinating and practical world of statistics through the introductory statistics course series at The Institute.

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Mrs. Meena Badade

Mrs. Meena Badade

Mrs. Meena Badade has over 23 years teaching experience, leading courses in statistics at various levels of education and at different institutions nationally and internationally. She also has a number of research papers published in respected journals. In addition to academic practice, she has considerable corporate experience at Metric Consultancy, where she worked as a statistical consultant and data analyst for international clients, applying various statistical techniques to projects in several industries.

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Course Syllabus

Week 1

Confidence Intervals for Proportions; 2-Sample Comparisons

  • CI for a proportion
  • The language of hypothesis testing
  • A-B tests
  • Bandit Algorithms (briefly)

Week 2

Correlation and Simple (1-variable) Regression

  • Correlation coefficient
  • Significance testing for correlation
  • Fitting a regression line by hand
  • Least squares fit
  • Using the regression equation

Week 3

Multiple Regression

  • Explain or predict?
  • Multiple predictor variables
  • Assessing the regression model
  • Goodness-of-fit (R-squared)
  • Interpreting the coefficients
  • RMSE (root mean squared error)

Week 4

Prediction; K-Nearest Neighbors

  • Using the regression model to make predictions
  • Using a hold-out sample
  • Assessing model performance
  • K-nearest neighbors

Class Dates

2021

Nov 5, 2021 to Dec 3, 2021

Dec 10, 2021 to Jan 7, 2022

2022

Jan 7, 2022 to Feb 4, 2022

Feb 11, 2022 to Mar 11, 2022

2023

No classes scheduled at this time.

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Prerequisites

We assume you are versed in statistics or the equivalent understanding of topics covered in our Statistics 1 and Statistics 2 courses. Review the course description for each of our introductory statistics courses and estimate which best matches your level, then take the self test for that course.

The courses listed below are prerequisites for enrollment in this course:

Generalized Linear Models Course

Statistics 1 – Probability and Study Design

This course, the first of a three-course sequence, provides an introduction to statistics for those with little or no prior exposure to basic probability and statistics.
Topic: Statistics, Introductory Statistics | Skill: Introductory | Credit Options: ACE, CAP, CEU
Class Start Dates: Nov 5, 2021, Dec 3, 2021, Jan 7, 2022, Feb 4, 2022

What Our Students Say​

Over the last two summers, I've taken the two stats courses meant to prepare future AP Stats teachers. Those courses were invaluable to me and gave me the confidence I needed to tackle a difficult subject.

Karen Behrend
Herbert Turner

I think the resampling approaches are refreshing and insightful. And the textbooks are marvelous in their clarity of expression and real world examples. I have told many of my colleagues about this wonderful and refreshing online medium for learning about statistics.

Herbert Turner
Analytica, Inc.

Frequently Asked Questions

Can I transfer or withdraw from a course?

We have a flexible transfer and withdrawal policy that recognizes circumstances may arise to prevent you from taking a course as planned. You may transfer or withdraw from a course under certain conditions.

  • Students are entitled to a full refund if a course they are registered for is canceled.
  • You can transfer your tuition to another course at any time prior to the course start date or the drop date, however a transfer is not permitted after the drop date.
  • Withdrawals on or after the first day of class are entitled to a percentage refund of tuition.

Please see this page for more information.

Who are the instructors at the Institute?

The Institute has more than 60 instructors who are recruited based on their expertise in various areas in statistics. Our faculty members are:

  • Authors of well-regarded texts in their area;
  • Advisory board members;
  • Senior faculty; and
  • Educators who have made important contributions to the field of statistics or online education in statistics.

The majority of our instructors have more than five years of teaching experience online at the Institute.

Please visit our faculty page for more information on each instructor at The Institute for Statistics Education.

Please see our knowledge center for more information.

What type of courses does the Institute offer?

The Institute offers approximately 80 courses each year. Topics include basic survey courses for novices, a full sequence of introductory statistics courses, bridge courses to more advanced topics. Our courses cover a range of topics including biostatistics, research statistics, data mining, business analytics, survey statistics, and environmental statistics.

Please see our course search or knowledge center for more information.

Do your courses have for-credit options?

Our courses have several for-credit options:

  • Continuing education units (CEU)
  • College credit through The American Council on Education (ACE CREDIT)
  • Course credits that are transferable to the INFORMS Certified Analytics Professional (CAP®)

Please see our knowledge center for more information.

Is the Institute for Statistics Education certified?

The Institute for Statistics Education is certified to operate by the State Council of Higher Education for Virginia (SCHEV). For more information visit: https://www.schev.edu/

Please see our knowledge center for more information.

Visit our knowledge base and learn more.

FAQs + Knowledge Base

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Additional Course Information

Organization of Course

This course takes place online at The Institute for 4 weeks. During each course week, you participate at times of your own choosing – there are no set times when you must be online. Course participants will be given access to a private discussion board. In class discussions led by the instructor, you can post questions, seek clarification, and interact with your fellow students and the instructor.

At the beginning of each week, you receive the relevant material, in addition to answers to exercises from the previous session. During the week, you are expected to go over the course materials, work through exercises, and submit answers. Discussion among participants is encouraged. The instructor will provide answers and comments, and at the end of the week, you will receive individual feedback on your homework answers.

Time Requirements

This is a 4-week course requiring 10-15 hours per week of review and study, at times of your choosing.

Homework

Homework in this course consists of short answer problems, and includes exercises that require the use of computer software.

In addition to assigned readings, this course also has an exam, short narrated software demos, example software codes, and  supplemental readings available online.

Exam
Those seeking ACE credit, or who are certificate candidates needing to satisfy their introductory statistics requirement, must pass an online exam at the end of Statistics 2 – Inference and Association.

Course Text

The text for this course is Introductory Statistics and Analytics: A Resampling Perspective by Peter Bruce, (2014, Wiley).  This course material will also be provided electronically, with updates, as part of the course, but you may wish to purchase the book as a reference to retain after the course is over.

Software

In this course, software is needed for statistical analysis and simple resampling/simulation operations. We recommend one of these three options:

  1. Regular Excel (not Excel Starter) and Resampling Stats for Excel (must have Windows)
  2. StatCrunch (Windows or Mac OS)
  3. R

Excel
You will need to have some facility with using formulas in Excel. If you don’t, please review either this tutorial or this tutorial before the course starts.

Resampling Stats for Excel
This is a commercial add-in for Excel, designed as a practitioner’s tool for doing resampling simulations. A free license is available to all course participants, while they are enrolled in the statistics.com sequence of introductory statistics courses. Runs only on Windows. Enrolled students will be given access to a free 1-year trial of Resampling Stats through the software download link on the main Stats course webpage.  You can also visit the Resampling Stats website and download the 1-year trial here.

StatCrunch
This is a very affordable web-based statistical software program, which also has simulation and resampling capabilities. Runs over the web, so can be used with both Windows and Mac. Resampling is not as intuitive as with Box Sampler and Resampling Stats for Excel.  Learn more at www.statcrunch.com.

Note for StatCrunch Users:  On all platforms, we recommend that you use the New version of StatCrunch.  All examples in the textbook supplement are based on the New version of StatCrunch.

R
R is a powerful opensource statistical scripting language that is widely recognized as an industry standard.  You will need to have familiarity with R and RStudio prior to taking the Statistics 1, 2 or 3 courses if you choose to use R as your software package.  Comprehensive supplemental materials are available for R users.  You can learn more about R here and RStudio here.

Python: Python is a language long used in computer science that has recently become quite popular in data science.  You will need to have familiarity with Python prior to taking the Statistics 1, 2 or 3 courses if you choose to use Python as your software package.  Comprehensive supplemental materials and support are available for Python users.  We recommend the use of Jupyter notebooks and the Anaconda installation package.

Software Uses and Descriptions | Available Free Versions
To learn more about the software used in this course, or how to obtain free versions of software used in our courses, please read our knowledge base article “What software is used in courses?” 

Course Fee & Information

Enrollment
Courses may fill up at any time and registrations are processed in the order in which they are received. Your registration will be confirmed for the first available course date unless you specify otherwise.

Transfers and Withdrawals
We have flexible policies to transfer to another course or withdraw if necessary.

Group Rates
Contact us to get information on group rates.

Discounts
Academic affiliation?  In most courses you are eligible for a discount at checkout.

New to Statistics.com?  Click here for a special introductory discount code.  

Invoice or Purchase Order
Add $50 service fee if you require a prior invoice, or if you need to submit a purchase order or voucher, pay by wire transfer or EFT, or refund and reprocess a prior payment.

Options for Credit and Recognition

This course is eligible for the following credit and recognition options:

No Credit
You may take this course without pursuing credit or a record of completion.

Mastery or Certificate Program Credit
If you are enrolled in mastery or certificate program that requires demonstration of proficiency in this subject, your course work may be assessed for a grade.

CEUs and Proof of Completion
If you require a “Record of Course Completion” along with professional development credit in the form of Continuing Education Units (CEU’s), upon successfully completing the course, CEU’s and a record of course completion will be issued by The Institute upon your request.

ACE CREDIT | College Credit
This course has been evaluated by the American Council on Education (ACE) and is recommended for college credit.  For recommendation details (level, and number of credits), please see this page. Please note that the decision to accept specific credit recommendations is up to the academic institution accepting the credit.

ACE Digital Badge
Courses evaluated by the American Council on Education (ACE) have a digital badge available for successful completion of the course.

Exam
Those seeking ACE credit, or who are certificate candidates needing to satisfy their introductory statistics requirement, must pass an online exam at the end of Statistics 2 – Inference and Association.

Supplemental Information

There is no supplemental content for this course.

Miscellaneous

There is no additional information for this course.

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Statistics 2 – Inference and Association
$449 | Enroll Now
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